CN115100848B - Ground traffic jam travel tracing method and system - Google Patents
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Abstract
The invention relates to a travel tracing method and a travel tracing system for ground traffic jams, wherein the method comprises the following steps: acquiring mobile phone signaling data and acquiring ground traffic OD information within a set time range; constructing a network path simulation model of the urban road by utilizing multi-source data integration; inputting the OD matrixes of all the trips into a network path simulation model, outputting to obtain space-time tracks of all the trips, and forming a data table; calculating the departure congestion contribution rate and the departure congestion time concentration of each departure grid, the arrival congestion contribution rate and the arrival congestion time concentration of each arrival grid, and the travel direction congestion contribution rate and the travel direction congestion time concentration of all combinations from the departure grid to the arrival grid according to the data table; and screening out a plurality of grids with the top ranking of each index to finish travel tracing. Compared with the prior art, the invention has the advantages of rapidness, low cost, high accuracy and the like.
Description
Technical Field
The invention relates to the field of traffic jam management, in particular to a travel tracing method and system for ground traffic jams.
Background
With rapid urban development, urban diseases such as traffic jams are increasingly highlighted, and a refined problem diagnosis method is urgently needed. The main cause of traffic congestion is that excessive vehicles occur simultaneously on the same road section for a short period of time. Vehicles that appear on congested road segments are themselves contributors to congestion. The travel cause of the traffic jam is precisely identified, namely, the position from where the vehicle arrives and where the vehicle arrives when each road section is jammed is traced back.
One of the possible ways to achieve this trace back is to use monitoring equipment (e.g. a city brain system) that covers the entire road network to continuously monitor and track the departure, arrival and path trajectories of vehicles located in congested road segments. However, this method is costly, labor intensive, complex, and may be privacy-infringing.
Other methods exist that can only achieve a coarser trace back. The invention of China with the patent number of CN201910197618.4 discloses a method for identifying urban traffic early-peak congestion sources based on mobile phone signaling. The main idea is to obtain the initial trip behavior information of an individual and calculate trip characteristic indexes by using mobile phone signaling data, and then identify and obtain the traffic early peak congestion source of a target city. However, this method still has the following problems: 1) The method only judges the possibility of congestion caused by concentrated travel in a short time through travel characteristic indexes (travel distance, travel direction and the like) of urban units, and does not consider the real condition of ground traffic congestion; 2) The method can not finely reflect the travel reasons causing the congestion of a certain road section or a certain road, and can only roughly analyze the congestion cause from the whole city level. 3) The method only considers the first trip from home, and omits the more complex trip situation after the departure.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a travel tracing method and system for ground traffic jam, so that the comprehensive and accurate travel tracing of the ground traffic jam is realized.
The aim of the invention can be achieved by the following technical scheme:
a travel tracing method for ground traffic jams comprises the following steps:
s1, acquiring mobile phone signaling data, and extracting ground traffic OD information in a set time range based on the mobile phone signaling data, wherein the ground traffic OD information comprises grids with set lengths and an OD matrix between the grids;
s2, constructing a network path simulation model of the urban road by utilizing multi-source data integration;
s3, inputting the OD matrixes of all the trips into a network path simulation model, outputting to obtain space-time tracks of all the trips, wherein the space-time tracks comprise trip numbers, departure grid numbers, arrival road section time, departure road section time and congestion situations, and forming a data table;
s4, calculating the departure congestion contribution rate and the departure congestion time concentration degree of each departure grid, the arrival congestion contribution rate and the arrival congestion time concentration degree of each arrival grid, and the travel direction congestion contribution rate and the travel direction congestion time concentration degree of all combinations from the departure grid to the arrival grid according to the data table; and screening out a plurality of grids with the top ranking of each index to finish travel tracing.
Further, the step S2 specifically includes:
s21, acquiring an urban road network data set through an open map data platform, wherein the data set comprises road vectors, road grades and lane direction information;
s22, adding non-congestion traffic speed information to the urban road network data set; the acquisition sources of traffic speed information in non-congestion comprise taxi space-time track data sets provided by a third party or commercial navigation map API;
s23, adding congestion situation and congestion time average traffic speed information to the urban road network data set; the congestion situation and the average traffic speed information during congestion are obtained from commercial navigation map API; the congestion situation is the congestion level of each road section at each moment;
s24, combining the processed urban road network data with a shortest time consumption path simulation algorithm to construct a network path simulation model of the urban road.
Further, in the step S22, when the taxi space-time trajectory data set is used, the taxi trajectory is matched to the road network, and the average speed of each road section in the non-congestion period is counted; when the commercial navigation map API is used, the average passing speed of each road section when the road section is not congested is obtained, and then the speed is added to the corresponding road section of the road network by a space matching method.
Further, in the step S23, the congestion status includes five levels of smoothness, creep, slight congestion, congestion and severe congestion; and adding information to the corresponding road section of the road network by a space matching method according to the congestion situation and the average traffic speed in the commercial navigation map API during congestion.
Further, in the step S3, the method for acquiring the space-time track includes: for each trip in the OD matrix, randomly selecting a space position in a departure grid and an arrival grid as an actual departure point and an actual arrival point of the trip, and inputting the departure point, the arrival point and the departure time into a network path simulation model to obtain a space-time track of the trip.
Further, in the step S4, theDegree of congestion contributionThe expression of (2) is:
wherein ,refers to the total mileage of congestion of travel contribution from grid i; />Refers to the congestion mileage contributed by the mth trip from grid i; />Means that the congestion mileage of all travel contributions from grid i is summed up; />Refers to the travel mileage of the mth trip from the grid i; />Is the total mileage of all trips from grid i;
reach Congestion contribution degreeIs represented by the expression:
wherein ,the total congestion mileage of travel contribution reaching the unit j; />Refers to the congestion mileage of the mth travel contribution to the unit j; />Means that the congestion mileage of all travel contributions reaching the grid i is summed up; />Refers to the travel mileage of the mth trip to the unit j; />Is the total mileage of all trips to grid i.
Further, in the step S4, a departure congestion time concentration degreeThe expression of (2) is:
wherein ,refers to the contribution of the travel of a certain grid i to the traffic jam in a certain period t>Total contribution of the unit in the whole early peak>Is a ratio of (3);
concentration of arrival congestion timeThe expression of (2) is:
wherein ,means that within a certain period t, the travel reaching grid j contributes +.>Total contribution of the unit in the whole early peak>Is a ratio of (2).
Further, in the step S4, a travel direction congestion contribution rate C ij The expression of (2) is:
wherein ,Lij Refers to a netThe total congestion mileage of all travel contributions in the direction from grid i to grid j; VMC m(ij) Refers to the congestion mileage contributed from the mth trip of grid i to grid j; sigma (sigma) m VMC m(ij) Means that the congestion mileage of all travel contributions from grid i to grid j are summed; VMT (virtual machine tool) m(ij) Refers to the mileage of the mth trip from grid i to grid j; sigma (sigma) m VMT m(ij) Is the sum of the total mileage of all trips from grid i.
Further, in the step S4, the travel direction congestion time concentration degree H ij The expression of (2) is:
H ij ＝-∑ t p(L ij_t )log(p(L ij_t ))
wherein, p (L ij_t ) Refers to the contribution L of the travel from grid i to grid j to traffic congestion in a certain period t ij_t The total contribution of the unit is sigma t L ij_t Is a ratio of (2).
The travel tracing system for the ground traffic jam comprises a processor and a memory, wherein the processor calls a program stored in the memory to execute the travel tracing method for the ground traffic jam.
Compared with the prior art, the invention has the following beneficial effects:
1. firstly, extracting early peak travel OD information by using mobile phone signaling data, and acquiring space-time tracks of all travel by adopting a network path simulation model of an urban road; secondly, constructing an urban road network model by utilizing a road network data set of an open map data platform, real-time road condition information of a commercial navigation map and a path simulation algorithm, and simulating a space-time track of any trip; then, based on the space-time track obtained by simulation and combining real-time road condition information (road congestion state); and finally, designing and calculating a reasonable index system, tracing the travel causing urban road congestion, and analyzing which travel causes the congestion on the whole and specific road section. Therefore, the invention comprehensively adopts the mobile phone signaling data, the traffic situation data and the path simulation technology to realize the trip tracing method of traffic jam with high speed, low cost and high accuracy.
2. The method has two acquisition modes in traffic speed information, namely, a taxi space-time track data set is adopted, a taxi track is matched with a road network, and the average speed of each road section in a non-congestion period is counted; and secondly, acquiring the average passing speed of each road section when the road section is not congested by using a navigation API of the map, and adding information to the corresponding road section of the road network by a space matching method. The first mode is generally adopted to have better accuracy, and the second mode can be adopted when the space-time track data set of the taxi is not available, so that the overall applicability of the method is improved.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
As shown in fig. 1, the embodiment discloses a travel tracing method for ground traffic jam, which comprises the following steps:
step S1, acquiring mobile phone signaling data, and extracting ground traffic travel OD information within a set time range based on the mobile phone signaling data, wherein the ground traffic travel OD information comprises grids with set lengths and travel OD matrixes among the grids.
And S2, constructing a network path simulation model of the urban road by utilizing multi-source data integration.
And S3, inputting the OD matrixes of all the trips into a network path simulation model to output to obtain space-time tracks of all the trips, wherein the space-time tracks comprise trip numbers, departure grid numbers, arrival road section time, departure road section time and congestion situations, and a data table is formed.
Step S4, calculating the departure congestion contribution rate and the departure congestion time concentration of each departure grid, the arrival congestion contribution rate and the arrival congestion time concentration of each arrival grid, and the travel direction congestion contribution rate and the travel direction congestion time concentration of all combinations from the departure grid to the arrival grid according to the data table; and screening out a plurality of grids with the top ranking of each index to finish travel tracing.
The specific development of each step is as follows:
1. in step S1, ground traffic "departure-arrival" (OD) information is extracted based on the mobile phone signaling data.
When acquiring the mobile phone signaling data, in order to avoid privacy problems, the mobile phone signaling data needs to be subjected to data cleaning, and then an active chain formed by residence places, employment places and stay points of early peak time periods of effective users and an early peak time period trip mode (ground traffic or subway) are identified.
Screening all travel related to a stay point with an early peak period (7:00-10:00), travel distance greater than 3km, ground traffic travel and stay time longer than half an hour, matching a departure place (O) and a Destination (D) of each travel to a 1km grid where each travel is located, obtaining an OD matrix between grids of 1km in the whole city, and marking the departure time and travel type (whether the travel is commuter travel) of each travel. The method is matched with a 1km grid, and is also used for avoiding leakage of private information such as specific trip positions of individuals.
2. In step S2, a network path simulation model for further path simulation is constructed using multi-source data integration.
(1) And acquiring a certain urban road network data set through an open map data platform OpenStreetMap, wherein the data set contains information such as road vectors, lane directions and the like.
(2) And adding non-congestion traffic speed information to the road network. The traffic speed information has two acquisition modes, namely, a taxi space-time track data set is adopted, a taxi track is matched with a road network, and the average speed of each road section in a non-congestion period is counted; and secondly, acquiring the average passing speed of each road section when the road section is not congested by adopting commercial navigation APIs (such as a hundred-degree map, a Goldmap and the like), and then adding the speed to the corresponding road section of the road network by a space matching method. Generally, the first approach is more accurate in the speed of traffic for each road segment. The second type may be taken when there is no taxi space-time trajectory dataset.
(3) And adding congestion situation and congestion time average passing speed information to the road network. The method comprises the steps of obtaining the congestion situation of each road section (namely the congestion level of each road section at each moment is generally divided into smooth, slow running, slight congestion, congestion and serious congestion) of each road section every 15 minutes in the same date and early peak period as mobile phone data and the average traffic speed when in congestion by using a hundred-degree map or a Goldmap API, and adding information to the corresponding road section of a road network by a space matching method.
(4) And constructing a network path simulation model of the urban road by using road network data (comprising road vectors, additional traffic speeds and congestion situations at all times) and a shortest time consumption path simulation algorithm (Dijkstra algorithm). The model can input the departure point, the arrival point and the departure time of any trip in the early peak period, and output the specific space-time track of the trip, including all the sections and the passing time.
3. In step S3, a path simulation of each trip is specifically performed.
For each trip in the OD matrix, randomly selecting a space position in a departure place and arrival place grid as an actual departure point and an arrival point, and inputting the departure point, the arrival point and the departure time into a network path simulation model of the urban road to obtain a specific space-time track of the trip.
Sequentially simulating the space-time track of the OD in all the early peak periods, recording the information such as the sequence, the space position (road section number), the passing time, the travel type and the like of the arrival of the OD in all the road sections, and forming the following data table:
4. in step S4, specifically analyzing and tracing the travel.
The following analytical criteria were defined:
(1) Degree of contribution of departure congestion
wherein ,the total congestion mileage of travel contribution from grid i is shown, the unit can be a 1km grid unit, or can be any larger space unit such as a street, a urban area and the like, and if the space unit is a larger unit, statistics are summarized according to the 1km grid unit covered by the larger unit. />Refers to the congestion mileage contributed by the mth trip from grid i, VMC means Vehicle Miles Congested. />Refers to summing the congestion mileage of all travel contributions from grid i. />The larger it means that the more congestion is caused by the travel from grid i.
(2) Reach Congestion contribution degree
wherein ,refers to the total mileage of congestion for travel contribution to grid j. />Refers to the congestion mileage of the mth travel contribution to reach grid j。/>Again, summing. />The larger indicates more congestion caused by travel to grid j.
(3) Degree of contribution to travel direction congestion
wherein ,Lij Refers to the total mileage of congestion for all travel contributions in this direction of grid i-grid j. The grid cells may be 1km grid cells as well, or may be any larger space cells such as streets, urban areas, etc. VMC m(ij) Refers to the congestion mileage contributed from the mth trip of grid i to grid j. Sigma (sigma) m VMC m(ij) Again, summing.The larger it means that the more congestion is caused by the travel in this direction of grid i-grid j.
(4) Departure congestion contribution rate
wherein ,referring to the range of the mth trip from grid i, VMT means Vehicle Miles Traveled. />Is the total mileage of all trips from grid i. />The larger the value of (c) indicates the more likely that the trip from i is to be congested.
(5) Arrival congestion contribution rate
wherein ,refers to the mileage of the mth trip to grid j. />Again, summing.The larger the value of (c) indicates the more likely congestion will be caused by travel to grid j.
(6) Travel direction congestion contribution rate
wherein ,VMTm(ij) Refers to the mileage from grid i to the mth trip of grid j. Sigma (sigma) m VMT m(ij) Again, summing. C (C) ij The larger the value of (c) indicates the more likely congestion is caused by the travel from grid i to grid j.
(7) Departure congestion time concentration
wherein ,refers to the contribution of the travel of a certain grid i to traffic congestion during a certain period of time (t, such as a period of time every 15 minutes)>Total contribution of the unit in the whole early peak>Is a ratio of (2). By reference to the definition of Shannon information entropy, < ->Refers to the concentration of the contribution of the travel of grid i to traffic congestion over time. />The larger the value, the more concentrated the impact of the unit on congestion is in time distribution.
(8) Concentration of arrival congestion time
wherein ,refers to the contribution of travel to the grid j to traffic congestion within a certain period of time (t, e.g. one time period every 15 minutes)>Total contribution of the unit in the whole early peak>Is a ratio of (2). By reference to the definition of Shannon information entropy, < ->Refers to the concentration in time of the contribution of travel to the traffic jam to reach grid j. />The larger the value, the more concentrated the impact of the unit on congestion is in time distribution.
(9) Travel direction congestion time concentration
H ij ＝-∑ t p(L ij_t )log(p(L ij_t ))
wherein ,p(Lij_t ) Refers to the contribution of travel from grid i to grid j to traffic congestion (L ij_t ) The unit total contribution (sigma) is taken up by the whole early peak t L ij_t ) Is a ratio of (2). Reference to definition of Shannon information entropy, H ij Refers to the concentration in time of the contribution of the travel from grid i to grid j to the traffic jam. H ij The larger the value, the more concentrated the effect of the travel from grid i to grid j on congestion is over time.
Based on the index calculated in the table 1, the basis is provided for the related traffic jam relief policy:
identifying departure congestion contributionDeparture Congestion contribution Rate->The larger units, i.e. the units with more accumulated congestion and higher congestion probability caused by the outgoing travel, can relieve the congestion by guiding the departure time of the residents of the place. Identifying arrival Congestion contribution +.>Arrival Congestion contribution Rate->The larger units, i.e. the more accumulated congestion the travel to the ground causes, the higher the congestion probability, the congestion can be relieved by guiding the arrival time of the residents of the ground. Identifying travel direction congestion contribution (L) ij ) Travel direction congestion contribution ratio (C ij ) Larger units may alleviate congestion by increasing the mass transit supply from grid i to grid j. By-> and Hij Further analyzing the time concentration of congestion contributions of departure, arrival and travel directions of each unit,/-> and Hij The higher value units of (a) can perform centralized traffic management in the corresponding departure or arrival most dense time period.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiment also provides a travel tracing system for ground traffic jam, which comprises a data set making module for executing step S2. And the travel path simulation module is used for executing the step S3. And the trip identification and analysis module is used for causing ground traffic jam and executing step S4.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (6)
1. The travel tracing method for the ground traffic jam is characterized by comprising the following steps of:
s1, acquiring mobile phone signaling data, and extracting ground traffic OD information within a set time range based on the mobile phone signaling data, wherein the ground traffic OD information comprises grids with set side lengths and a travel OD matrix between the grids;
s2, constructing a network path simulation model of the urban road by utilizing multi-source data integration;
s3, inputting the OD matrixes of all the trips into a network path simulation model, outputting to obtain space-time tracks of all the trips, wherein the space-time tracks comprise trip number departure grid numbers, arrival road section time, departure road section time and congestion situations, and forming a data table;
s4, calculating the departure congestion contribution rate and the departure congestion time concentration degree of each departure grid, the arrival congestion contribution rate and the arrival congestion time concentration degree of each arrival grid, and the travel direction congestion contribution rate and the travel direction congestion time concentration degree of all combinations from the departure grid to the arrival grid according to the data table; screening a plurality of grids with top ranking of each index to finish travel tracing;
in the step S4, the congestion contribution degree is calculatedThe expression of (2) is:
wherein ,refers to the total congestion of travel contributions from grid iMileage; />Refers to the congestion mileage contributed by the mth trip from grid i; />Means that the congestion mileage of all travel contributions from grid i is summed up; />Refers to the travel mileage of the mth trip from the grid i; />Is the total mileage of all trips from grid i;
reach Congestion contribution degreeIs represented by the expression:
wherein ,the total congestion mileage of travel contribution reaching the unit j; />Refers to the congestion mileage of the mth travel contribution to the unit j; />Means that the congestion mileage of all travel contributions reaching the grid i is summed up; />Refers to the travel mileage of the mth trip to the unit j; />Is the total mileage of all trips to grid i;
in the step S4, the departure congestion time concentration degreeThe expression of (2) is:
wherein ,refers to the contribution of the travel of a certain grid i to the traffic jam in a certain period t>Total contribution of the unit in the whole early peak>Is a ratio of (3);
concentration of arrival congestion timeThe expression of (2) is:
wherein ,means that within a certain period t, the travel reaching grid j contributes +.>Total contribution of the unit in the whole early peak>Is a ratio of (3);
in the step S4, the travel direction congestion contribution rate C ij The expression of (2) is:
wherein ,Lij The congestion total mileage of all travel contributions in the direction from grid i to grid j is referred to; VMC m(ij) Refers to the congestion mileage contributed from the mth trip of grid i to grid j; sigma (sigma) m VMC m(ij) Means that the congestion mileage of all travel contributions from grid i to grid j are summed; VMT (virtual machine tool) m(ij) Refers to the mileage of the mth trip from grid i to grid j; sigma (sigma) m VMT m(ij) Is the sum of the total mileage of all the trips from the grid i;
in the step S4, the travel direction congestion time concentration degree H ij The expression of (2) is:
H ij ＝-∑ t p(L ij_t )log(p(L ij_t ))
wherein, p (L ij_t ) Refers to the contribution L of the travel from grid i to grid j to traffic congestion in a certain period t ij_ The total contribution of the unit is sigma t L ij_ Is a ratio of (2).
2. The travel tracing method of ground traffic jam according to claim 1, wherein the step S2 specifically includes:
s21, acquiring an urban road network data set through an open map data platform, wherein the data set comprises road vectors, road grades and lane direction information;
s22, adding non-congestion traffic speed information to the urban road network data set; the acquisition sources of traffic speed information in non-congestion comprise taxi space-time track data sets provided by a third party or commercial navigation map API;
s23, adding congestion situation and congestion time average traffic speed information to the urban road network data set; the congestion situation and the average traffic speed information during congestion are obtained from commercial navigation map API; the congestion situation is the congestion level of each road section at each moment;
s24, combining the processed urban road network data with a shortest time consumption path simulation algorithm to construct a network path simulation model of the urban road.
3. The method for tracing the travel of the ground traffic jam according to claim 2, wherein in the step S22, when the space-time trajectory dataset of the taxis is used, the taxis trajectory is matched with the road network, and the average speed of each road section in the non-jam period is counted; when the commercial navigation map API is used, the average passing speed of each road section when the road section is not congested is obtained, and then the speed is added to the corresponding road section of the road network by a space matching method.
4. The ground traffic jam travel tracing method according to claim 2, wherein in the step S23, the jam situation includes five levels of clear, slow, slight, jam and severe jam; and adding information to the corresponding road section of the road network by a space matching method according to the congestion situation and the average traffic speed in the commercial navigation map API during congestion.
5. The ground traffic jam travel tracing method according to claim 1, wherein in the step S3, the space-time trajectory obtaining manner includes: for each trip in the OD matrix, randomly selecting a space position in a departure grid and an arrival grid as an actual departure point and an actual arrival point of the trip, and inputting the departure point, the arrival point and the departure time into a network path simulation model to obtain a space-time track of the trip.
6. The travel tracing system for the ground traffic jam is characterized by comprising a processor and a memory, wherein the processor calls a program stored in the memory to execute the travel tracing method for the ground traffic jam according to any one of claims 1-5.
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